PyraFuse is a DINOv3-based semantic-segmentation framework for three classes: 0 = background, 1 = fabric, and 2 = skin. The associated paper has been accepted at AIMLSystems 2026.

Each folder is self-contained: config.json, decoder.pt, ema.pt, and a backbone/ directory with the DINOv3 configuration, model.safetensors, and adapter feature-normalisation weights. Use EMA weights for evaluation.

Install the PyraFuse code repository, then load any variant. Pin a revision for reproducible experiments.

Inputs must be RGB, ImageNet-normalised, and have height and width divisible by 16. The default checkpoint configuration uses 448 × 448 inputs.

These eager PyTorch checkpoint bundles are the portable source of truth. TensorRT engines are deliberately not distributed here because they are tied to the target GPU, CUDA, TensorRT version, precision, and optimization profile. Build and validate TensorRT engines on the deployment host from the PyTorch checkpoint using the repository's scripts/export_trt.py command.

The PyraFuse decoder and repository code are Apache-2.0. Each checkpoint bundle includes Meta DINOv3 backbone material; use and redistribution of those bundles are additionally subject to the DINOv3 License. A verbatim copy is provided at LICENSES/DINOv3-LICENSE.md in this model repository. The underlying Fashionpedia and visuAAL datasets are not redistributed and retain their own terms.

Training labels fuse Fashionpedia annotations with visuAAL skin masks. The source data is not redistributed in this repository; users must comply with the source datasets' terms. Model performance can vary by image quality, lighting, occlusion, garment appearance, and representation in source data. This model is for research and engineering use, not a medical or biometric decision system.

The accepted manuscript is included in the code repository. Formal citation metadata will be added after the AIMLSystems 2026 camera-ready publication.